Final Technical Report: - Southwest Fisheries Science Center - NOAA

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used to evaluate the ability of summer/fall models to predict winter/spring cetacean density patterns (Section 3.8). 3.1.2 In situ Oceanographic Measurements Oceanographic variables were measured on NMFS cetacean and ecosystem assessment surveys in the ETP during 1986-2006 and in the CCE during 1991-2005. Sea surface temperature (SST) and salinity (SSS) from a thermosalinograph were recorded continuously at 0.5 to 2 minute intervals and averaged over 5-10 km intervals to reduce both the number of observations and the discrepancy in sample spacing along and between transects. Thermocline depth (TD, depth of maximum temperature gradient in a 10 m interval), thermocline strength (TS, ºC m -1 ), and mixed layer depth (MLD, the depth at which temperature is 0.5ºC less than surface temperature) were estimated from expendable bathythermograph (XBT) and conductivitytemperature-depth (CTD) casts collected three to five times per day. Surface chlorophyll (CHL, mg m -3 ) was estimated at the same stations from the surface bottle on the CTD or from bucket samples analyzed by standard techniques (Holm-Hansen et al. 1965). CHL was log-transformed (using natural logarithms) to normalize the data for interpolation. Details of the field methods can be found in Philbrick et al. (2001, 2003). 3.1.3 Remotely Sensed Oceanographic Measurements Remotely sensed sea surface temperature (SST) data were considered for models within the California Current Ecosystem. Models included SST and measures of its variance as potential predictors. SST data (National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service/Pathfinder v5) were obtained via an OPeNDAP server using Matlab code that enabled remote, automated downloading of data for user-specified positions and resolutions. As part of this analysis (Becker 2007), we examined the predictive power of six different spatial resolutions of satellite SST data ranging from one pixel (approximately 31 km 2 ) to 36 pixels (approximately 1,109 km 2 ). Three temporal resolutions were also compared: 1) 1-day, 2) 8-day, and 3) 30-day composites. We used the coefficient of variation of SST, CV(SST), for resolutions greater than one pixel as a proxy for frontal regions in the California Current study area. Results are summarized below and details can be found in Becker (2007). Our SST temporal resolution analysis for the satellite-derived data indicated that, while 30-day SST composites had good within-dataset explanatory ability, predictive ability across datasets was poor at this coarser temporal resolution. A correlation analysis showed high correlation between the 1-day and 8-day SST values (R 2 = 0.96), indicating that the 8-day composites provided adequate representation of average conditions on the day of the survey. Based on this evaluation and the greater availability of 8-day composite data compared to 1-day composites, we selected 8-day running average SST composites, centered on the date of each survey segment. 8
The SST spatial resolution comparison indicated that, for the majority of species, the greatest predictive ability was observed for the coarsest SST spatial resolution (Table 1). The predictive ability of different spatial resolutions of satellite-derived CV(SST) was more variable than that of SST. For many species, the best CV(SST) spatial resolution was among the finer resolutions considered in this study, perhaps reflecting the importance of localized upwelling events or small-scale frontal features. Table 1. Summary of satellite-derived sea surface temperature (SST) and CV(SST) spatial resolutions selected for ten California Current Ecosystem species. Numbers refer to the number of pixels included in the resolution. The spatial resolutions tested included 1, 4, 9, 16, 25, and 36 pixels, corresponding to 5.55-33.3 km boxes (i.e., 30.8 – 1,108.9 km 2 ). Models are described in more detail in Section 3.3. Past studies have shown relationships between cetacean sightings and other remotely sensed measures such as chlorophyll (Smith et al. 1986, Jaquet et al. 1996, Moore et al. 2002). However, satellite-derived measures of chlorophyll concentration were not available for 3 of the 4 survey years used to develop our CCE habitat models. The Coastal Zone Color Scanner (CZCS), one of the first satellite sensors to collect ocean color data, ceased operation in 1986 and the Sea Wide-Field-of-View Sensor (SeaWiFS) began operating shortly after our 1996 cetacean survey was over. Since chlorophyll data were not available for most of our time series, we did not include this variable as a potential predictor in our habitat models. 3.1.4 Water Depth and Bottom Slope Water depth was derived from the ETOPO2 2-minute global relief data (U.S. Department of Commerce 2006), re-gridded to match the pixel resolutions used for modeling. Slope was calculated as the magnitude of the bathymetry gradient using the gradient operator tool in Generic Mapping Tools (Wessel and Smith 1998). Depth and slope values for each geographic location were obtained using the “sample” tool in ArcGIS (version 9.2, ESRI, Inc.). 9
Page 1 and 2: U N I R A P E D T T E M D S E N TA
Page 3: C O L A N IO T A N U N C I EA .S. D
Page 6 and 7: This report was prepared under cont
Page 8 and 9: Table of Contents Acronyms and Abbr
Page 10 and 11: C.1 Journal Publications ..........
Page 12 and 13: Figure 18. Encounter rate models bu
Page 14 and 15: List of Tables Table 1. Summary of
Page 16 and 17: Appendix B, Table B-6. Summary of m
Page 18 and 19: Acknowledgements This project was f
Page 20 and 21: of these choices as possible and us
Page 22 and 23: Although our models include most of
Page 24 and 25: 2.0 Background The Navy and other m
Page 26 and 27: comparison is based on a separate s
Page 28 and 29: Figure 1. Transects (green lines) s
Page 32 and 33: 3.1.5 Mid-trophic Sampling with Net
Page 34 and 35: variable. Interpolated maps of the
Page 36 and 37: Table 2. Variogram model results. A
Page 38 and 39: “number of individuals” as the
Page 40 and 41: Encounter Rate and Group Size Model
Page 42 and 43: were built separately for each of t
Page 44 and 45: Expanding models to the entire U.S.
Page 46 and 47: Table 4. Summary of the weighted ef
Page 48 and 49: Consistent with Barlow and Forney (
Page 50 and 51: incorporation of geographic coordin
Page 52 and 53: overwhelming majority of “Bryde
Page 54 and 55: the genera Ziphius and Mesoplodon.
Page 56 and 57: Table 7. Geographically stratified
Page 58 and 59: above, and the data themselves, are
Page 60 and 61: (hereafter “summer”). SWFSC has
Page 62 and 63: avoid these problems, we decided to
Page 64 and 65: Figure 9. Thermocline depth (m) obs
Page 66 and 67: Attempts to adjust search parameter
Page 68 and 69: CAMMS 1991 PODS 1993 ORCAWALE 1996
Page 70 and 71: 4.2 Modeling Framework : GLM and GA
Page 72 and 73: 4.2.4 Conclusions Regardings Modeli
Page 74 and 75: Table 10 cont. Comparison of the si
Page 76 and 77: Table 11 cont. Comparison of the si
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A) Striped dolphin 10 km B) Short-b
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dependent. For example, the number
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Figure 19. Predicted average densit
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ecosystem, eastern spinner dolphins
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Table 13. Variables selected for mo
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Table 14. Starting and final AIC va
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Table 17. Ratios of observed to pre
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species represented in the acoustic
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0.29 (Risso’s dolphin) to 3.20 (n
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Figure 25. Sample 2005 validation p
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Table 18. (continued) 1991 1993 Nor
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Blue whales had the greatest deviat
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Table 20. Abundance (number of anim
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Table 22. Proportion of deviance ex
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Figure 27. Average density (AveDens
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Table 23. Effective degrees of free
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We attempted to build encounter rat
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5.0 Conclusion The field of predict
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6.0 Transition Plan The models of c
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needed to ensure that the SDSS rema
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Administrative Report LJ-07-02. NOA
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Forney KA, Barlow J (1993) Prelimin
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Redfern JV, Barlow J, Ballance LT,
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Appendix A: Detailed Model Results
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Table A-1 (continued) Blue whale 19
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Figure A-1a. Striped dolphin 108
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Figure A-1c. Risso’s dolphin 110
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Figure A-1e. Northern right whale d
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Figure A-1g. Sperm whale 114
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Figure A-1i. Blue whale 116
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Figure A-1k. Baird’s beaked whale
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Figure A-2. Predicted average densi
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Figure A-2b. Short-beaked common do
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Figure A-2d. Pacific white-sided do
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Figure A-2f. Dall’s porpoise 126
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Figure A-2h. Fin whale 128
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Figure A-2j. Humpback whale 130
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Figure A-2l. Small beaked whales 13
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Table B-2. Summary of model validat
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Table B-10. Summary of model valida
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Figure B-1. Predicted yearly and av
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Figure B-1. b) Eastern spinner dolp
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Figure B-1. c) Whitebelly spinner d
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Figure B-1. d) Striped dolphin 168
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Figure B-1. e) Rough-toothed dolphi
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Figure B-1. f) Short-beaked common
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Figure B-1. g) Bottlenose dolphin 1
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Figure B-1. h) Risso’s dolphin 17
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Figure B-1. i) Cuvier’s beaked wh
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Figure B-1. j) Blue whale 180
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Figure B-1. k) Bryde’s whale 182
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Figure B-1. l) Short-finned pilot w
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Figure B-1. m) Dwarf sperm whale 18
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Figure B-1. n) Mesoplodon beaked wh
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Figure B-1. o) Small beaked whales
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Figure B-2. Predicted average densi
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Figure B-2. (cont.) c) Whitebelly s
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Figure B-2. (cont.) g) Bottlenose d
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Figure B-2. (cont.) k) Bryde’s wh
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Figure B-2. (cont.) o) Small beaked
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Ferguson MC (2005) Cetacean Populat
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Barlow J, Kahru M, Mitchell BG (In
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Memorandum NMFS-SWFSC-374, U.S. Dep
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